org.deeplearning4j.nn.conf.layers.samediff

Class AbstractSameDiffLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

BaseSameDiffLayer

public abstract class AbstractSameDiffLayer
extends Layer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AbstractSameDiffLayer.Builder<T extends AbstractSameDiffLayer.Builder<T>>

Field Summary

Fields

Modifier and Type	Field and Description
protected org.nd4j.linalg.learning.config.IUpdater	biasUpdater
protected double	l1
protected double	l1Bias
protected double	l2
protected double	l2Bias
protected org.nd4j.linalg.learning.config.IUpdater	updater

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

constraints, iDropout, layerName

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	AbstractSameDiffLayer()
protected	AbstractSameDiffLayer(AbstractSameDiffLayer.Builder builder)

Method Summary

Modifier and Type	Method and Description
void	applyGlobalConfig(NeuralNetConfiguration.Builder b)
abstract void	applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig) Apply the global configuration (weight init, activation function, etc) to this layer
abstract void	defineParameters(SDLayerParams params) Define the parameters for the network.
double	getL1ByParam(String paramName) Get the L1 coefficient for the given parameter.
double	getL2ByParam(String paramName) Get the L2 coefficient for the given parameter.
SDLayerParams	getLayerParams()
LayerMemoryReport	getMemoryReport(InputType inputType) This is a report of the estimated memory consumption for the given layer
abstract InputType	getOutputType(int layerIndex, InputType inputType) For a given type of input to this layer, what is the type of the output?
abstract InputPreProcessor	getPreProcessorForInputType(InputType inputType) For the given type of input to this layer, what preprocessor (if any) is required? Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor
org.nd4j.linalg.learning.config.IUpdater	getUpdaterByParam(String paramName) Get the updater for the given parameter.
abstract void	initializeParameters(Map<String,org.nd4j.linalg.api.ndarray.INDArray> params) Set the initial parameter values for this layer, if required
ParamInitializer	initializer()
protected void	initWeights(int fanIn, int fanOut, WeightInit weightInit, org.nd4j.linalg.api.ndarray.INDArray array)
abstract Layer	instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
boolean	isPretrainParam(String paramName) Is the specified parameter a layerwise pretraining only parameter? For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop. Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
char	paramReshapeOrder(String param) Returns the memory layout ('c' or 'f' order - i.e., row/column major) of the parameters.
abstract void	setNIn(InputType inputType, boolean override) Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

clone, initializeConstraints, resetLayerDefaultConfig

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

l1

protected double l1

l2

protected double l2

l1Bias

protected double l1Bias

l2Bias

protected double l2Bias

updater

protected org.nd4j.linalg.learning.config.IUpdater updater

biasUpdater

protected org.nd4j.linalg.learning.config.IUpdater biasUpdater

Constructor Detail

AbstractSameDiffLayer

protected AbstractSameDiffLayer(AbstractSameDiffLayer.Builder builder)

AbstractSameDiffLayer

protected AbstractSameDiffLayer()

Method Detail

getLayerParams

public SDLayerParams getLayerParams()

getOutputType

public abstract InputType getOutputType(int layerIndex,
                                        InputType inputType)

Description copied from class: Layer

For a given type of input to this layer, what is the type of the output?

Specified by:

getOutputType in class Layer

Parameters:

layerIndex - Index of the layer

inputType - Type of input for the layer

Returns:

Type of output from the layer

setNIn

public abstract void setNIn(InputType inputType,
                            boolean override)

Description copied from class: Layer

Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Specified by:

setNIn in class Layer

Parameters:

inputType - Input type for this layer

override - If false: only set the nIn value if it's not already set. If true: set it regardless of whether it's already set or not.

getPreProcessorForInputType

public abstract InputPreProcessor getPreProcessorForInputType(InputType inputType)

Description copied from class: Layer

For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor

Specified by:

getPreProcessorForInputType in class Layer

Parameters:

inputType - InputType to this layer

Returns:

Null if no preprocessor is required, otherwise the type of preprocessor necessary for this layer/input combination

defineParameters

public abstract void defineParameters(SDLayerParams params)

Define the parameters for the network. Use SDLayerParams.addWeightParam(String, int...) and SDLayerParams.addBiasParam(String, int[])

Parameters:

params - Object used to set parameters for this layer

initializeParameters

public abstract void initializeParameters(Map<String,org.nd4j.linalg.api.ndarray.INDArray> params)

Set the initial parameter values for this layer, if required

Parameters:

params - Parameter arrays that may be initialized

applyGlobalConfigToLayer

public abstract void applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)

Apply the global configuration (weight init, activation function, etc) to this layer

Parameters:

globalConfig - Global configuration

instantiate

public abstract Layer instantiate(NeuralNetConfiguration conf,
                                  Collection<TrainingListener> trainingListeners,
                                  int layerIndex,
                                  org.nd4j.linalg.api.ndarray.INDArray layerParamsView,
                                  boolean initializeParams)

Specified by:

instantiate in class Layer

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

Returns:

The parameter initializer for this model

getL1ByParam

public double getL1ByParam(String paramName)

Description copied from class: Layer

Get the L1 coefficient for the given parameter. Different parameters may have different L1 values, even for a single .l1(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL1ByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

L1 value for that parameter

getL2ByParam

public double getL2ByParam(String paramName)

Description copied from class: Layer

Get the L2 coefficient for the given parameter. Different parameters may have different L2 values, even for a single .l2(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL2ByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

L2 value for that parameter

getUpdaterByParam

public org.nd4j.linalg.learning.config.IUpdater getUpdaterByParam(String paramName)

Description copied from class: Layer

Get the updater for the given parameter. Typically the same updater will be used for all updaters, but this is not necessarily the case

Overrides:

getUpdaterByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

IUpdater for the parameter

isPretrainParam

public boolean isPretrainParam(String paramName)

Description copied from class: Layer

Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

Specified by:

isPretrainParam in class Layer

Parameters:

paramName - Parameter name/key

Returns:

True if the parameter is for layerwise pretraining only, false otherwise

getMemoryReport

public LayerMemoryReport getMemoryReport(InputType inputType)

Description copied from class: Layer

This is a report of the estimated memory consumption for the given layer

Specified by:

getMemoryReport in class Layer

Parameters:

inputType - Input type to the layer. Memory consumption is often a function of the input type

Returns:

Memory report for the layer

paramReshapeOrder

public char paramReshapeOrder(String param)

Returns the memory layout ('c' or 'f' order - i.e., row/column major) of the parameters. In most cases, this can/should be left

Parameters:

param - Name of the parameter

Returns:

Memory layout ('c' or 'f') of the parameter

initWeights

protected void initWeights(int fanIn,
                           int fanOut,
                           WeightInit weightInit,
                           org.nd4j.linalg.api.ndarray.INDArray array)

applyGlobalConfig

public void applyGlobalConfig(NeuralNetConfiguration.Builder b)